The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs.

The book’s main features are as follows:

The content is written in an easy-to-follow and self-contained style.
The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.
The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.
Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.
Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.
This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.

About the Author

Joe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.

He is the author of a series of textbooks in machine learning published by Springer.

- Statistical Learning with Math and R

- Statistical Learning with Math and Python

- Sparse Estimation with Math and R

- Sparse Estimation with Math and Python

- Kernel Methods for Machine Learning with Math and R (This book)

- Kernel Methods for Machine Learning with Math and Python

"About this title" may belong to another edition of this title.

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